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After Path C confirmed aux head's 28% accuracy at H=60 is the H6 Phase 3 bottleneck (not the mechanism itself), enlarge aux capacity: - AUX_HIDDEN_DIM: 32 -> 256 (8x, matches input dim, removes bottleneck) - AUX_TRUNK_H2: 128 -> 256 (2x, uniform trunk width) Architecture changes: - Aux head: 256 -> Linear -> 256 -> ELU -> Linear -> 2 (was 256->32->2) - Aux trunk: 256 -> 256 -> 256 -> 256 (was 256 -> 256 -> 128 -> 256) - +160K params total (mostly aux_nb_w1 + aux_rg_w1: [256, 256] each) Side effects: - Checkpoint fingerprint change (intentional) - Thread utilisation improves: AUX_BLOCK=256 threads x H=256 = 1:1 (vs 8:1 at H=32 — most threads idle previously) Phase 3 mechanism stays DORMANT (W=0, beta=0) for this validation smoke. Verdict criteria: aux_dir_acc improves from 0.28 toward 0.50+ with the larger capacity. If yes, re-activate Phase 3 priors. If no, the bottleneck is signal/horizon, not capacity. Cargo build clean (full nvcc rebuild). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Foxhunt
Production HFT trading system in Rust.
Architecture
The workspace contains 32 crates organized as follows:
Core Libraries (16)
| Crate | Purpose |
|---|---|
trading_engine |
Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing |
risk |
VaR, Kelly, circuit breakers, kill switches, compliance |
risk-data |
Risk data types and shared structures |
trading-data |
Trading data types |
ml |
DQN Rainbow, PPO, TFT, Mamba2, ensemble inference |
ml-data |
ML data types and feature definitions |
data |
Market data ingestion and storage |
backtesting |
Replay engine, strategy tester |
adaptive-strategy |
Ensemble execution, microstructure analysis |
common |
Shared types, resilience, error handling |
storage |
S3 and local model storage |
model_loader |
Model serialization and loading |
market-data |
Market data feed handlers |
database |
PostgreSQL access layer (SQLx) |
config |
Configuration management |
tli |
CLI commands and tooling |
Services (8)
| Service | Purpose |
|---|---|
backtesting_service |
gRPC backtesting service |
broker_gateway_service |
FIX routing, broker connectivity |
trading_service |
Core trading operations |
ml_training_service |
Model training orchestration |
data_acquisition_service |
Market data acquisition |
trading_agent_service |
Autonomous trading agents |
api_gateway |
gRPC API gateway with auth |
web-gateway |
Axum REST + WebSocket gateway |
Frontend
web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.
Building
# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace
# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib
# Clippy
SQLX_OFFLINE=true cargo clippy --workspace
ML Models
Four production model architectures on Candle v0.9.1 with CUDA:
- DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
- PPO -- Proximal Policy Optimization with GAE and LSTM policies
- TFT -- Temporal Fusion Transformer for multi-horizon forecasting
- Mamba2 -- State space model for sequence prediction
Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.
Infrastructure
- Git: Gitea at
git.fxhnt.ai(Tailscale-only), Scaleway DEV1-S - Observability: OpenTelemetry OTLP (env
OTEL_EXPORTER_OTLP_ENDPOINT) - Database: PostgreSQL with SQLx offline mode for CI
License
Proprietary. All rights reserved.